Thousands of men diagnosed with prostate cancer (CaP) in the United States undergo radical prostatectomy (RP) each year. For a significant proportion of these patients, biochemical recurrence (BCR) of the prostate cancer occurs within five years of surgery. Consequently, detecting BCR shortly after surgery may facilitate determining whether other treatments are necessary, and, if necessary, are initiated. Gleason scoring is a pathological grading system based on visual analysis by a pathologist of glandular and nuclear morphology. Gleason scoring is currently regarded as the best biomarker for predicting CaP aggressiveness and long-term post-surgical patient outcome. However, the post-surgical outcome of CaP patients with the same intermediate Gleason scores can vary significantly. Furthermore, Gleason scoring is subjective and is, therefore, susceptible to considerable inter-reviewer variability. Due to these limitations of Gleason scoring, other post-operative nomograms have been developed for predicting CaP aggressiveness and long-term post-surgical patient outcome.
Conventional nomograms may incorporate additional clinical variables to assist in predicting CaP aggressiveness and long-term post-surgical patient outcome. For example, tumor stage, pre-operative prostate specific antigen (PSA), and positive surgical margins have been integrated into the Kattan nomogram by Kattan et al., Postoperative nomogram for disease recurrence after radical prostatectomy for prostate cancer, J. Clinical Oncology, 17(5): 1499-1499, 1999. The Han Tables use the Gleason sum, tumor stage, and pre-operative PSA to construct a series of probability tables. Han et al., Biochemical (prostate specific antigen) recurrence probability following radical prostatectomy for clinically localized prostate cancer, J. Urol, 169(2):517-523, February 2003. Adding the date of surgery to a nomogram as a prognostic variable was described by Stephenson et al., in J. Clinical Oncology, 23(28):7005-7012, 2005. CARPA, developed at the University of California at San Francisco, separates post-operative CaP patients into risk categories and incorporates the percentage of positive biopsy cores into its risk assessment. Cooperberg et al., Multi-institutional validation of the UCSF cancer of the prostate risk assessment for prediction of recurrence after radical prostatectomy, Cancer, 107(10):2384-2391, 2006. The Memorial Sloan Kettering Cancer Center (MS-KCC) nomogram incorporates age and time free of cancer. Hinev et al., Validation of pre- and postoperative nomograms used to predict the pathological stage and prostate cancer recurrence after radical prostatectomy: a multi-institutional study, J. BU ON.: official journal of the Balkan Union of Oncology, 16(2):316, 2011. These conventional nomograms all rely on Gleason scoring, and consequently suffer from the inter-reviewer variability and subjectivity that affects the predictive value of Gleason scoring.
The advent of digital whole-slide scanners has allowed the digitization of tissue slides. Digitized slide images have been subjected to quantitative histomorphometry (QH), which applies computational tools to describe, classify, and diagnose disease patterns from the images. However, QH has conventionally been modeled after pathological Gleason grading. For example, morphological descriptors including gland size and perimeter ratio have been used in automated grading systems to distinguish between benign and malignant regions. Similarly, image texture has been used to characterize the appearance of CaP morphology. Extracting second-order image intensity texture features from co-occurrence matrices was described in Jafari-Khouzani et. al., Multiwavelet grading of pathological images of prostate, IEEE Trans on Biomedical Engineering, 50(6):697-704, 2003. Co-occurrence matrices have been used to evaluate the frequency with which two image intensities appear within a pre-defined distance of each other within a region of interest. First and second-order statistical features can be extracted to describe the local image texture. Haralick et al., Textural features for image classification, IEEE Trans on Systems, Man and Cybernetics, 3(6):610-621, 1973. For example, U.S. Pat. No. 8,634,610 disclosed a probabilistic assessment determined through the use of a logistic regression model based on a texture analysis of an image of a region of interest. However, texture features may suffer from a lack of transparency and interpretability.
Attempts to model CaP appearance have used the spatial arrangement of individual nuclei and glands. Color, texture, and structural morphology have been used to perform automated Gleason scoring, while nuclear roundness variance has been used to predict BCR. Graph networks have also been used to characterize the spatial arrangement of nuclei and glands. For example, using Voronoi and Delaunay-based graph tessellations to describe tissue architecture in CaP histology is described by Christens-Barry and Partin, Quantitative grading of tissue and nuclei in prostate cancer for prognosis prediction, Johns Hopkins Apl. Technical Digest, 18:226-233, 1997. Minimum spanning trees have been shown to strongly correlate with Gleason scoring by Doyle et al., Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer, BMC bioinformatics, 13(1):282, 2012. However, these conventional techniques all rely on fully connected graphs in which nuclei embedded in stromal and epithelial regions are connected in the graphs. This connecting of stromal and epithelial nuclei results in features extracted from conventional graphs representing averaged attributes of both stromal and epithelial regions. Unfortunately, such fully connected graphs are not sensitive to local glandular organization. Thus, while conventional methods for predicting BCR in prostate cancer patients have incorporated additional clinical variables, utilized QH, and explored image textures and fully-connected graphs to assist in predicting CaP aggressiveness, conventional methods still suffer from the drawbacks of those techniques.